As Exploring Transfer enters its 32nd year, we pause to re-articulate our mission as a summer intensive learning opportunity for community college students seeking classroom and residential experiences at a four-year liberal arts college. Over the previous three decades, we have sought to provide students with a rigorous academic environment that engages their hearts and minds in an experience in line with the mission of Vassar College:

The mission of Vassar College is to make accessible “the means of a thorough, well-proportioned and liberal education”* that inspires each individual to lead a purposeful life. The College makes possible an education that promotes analytical, informed, and independent thinking and sound judgment; encourages articulate expression; and nurtures intellectual curiosity, creativity, respectful debate and engaged citizenship. Vassar supports a high standard of engagement in teaching and learning, scholarship and artistic endeavor; a broad and deep curriculum; and a residential campus that fosters a learning community. Founded in 1861 to provide women an education equal to that once available only to men, Vassar is now open to all and strives to pursue diversity, inclusion, and equity as essential components of a rich intellectual and cultural environment in which all members, including those from underrepresented and marginalized groups, are valued and empowered to thrive. *From the College’s First Annual Catalogue. (
http://info.vassar.edu/about/vassar/mission.html
)

Vassar College, thus, promises an educational experience characterized by a liberal arts education. In Exploring Transfer, summer 2018, we will strive to embrace the liberal arts education mission as it aligns with our own Vassar College mission.

Exploring Transfer acknowledges its place as an initiative designed to increase access to a four-year college degree by historically underrepresented students. In fact, Exploring Transfer is one of the programs at Vassar College positioned to increase and support its own rapidly diversifying student population. Indeed, Exploring Transfer alumni, all of whom are drawn from populations underrepresented in higher education and all of whom are first generation to college, have gone on to complete bachelor’s degrees at the State and City Universities of New York, as well as Vassar College and other private institutions.

Our goal is to immerse Exploring Transfer students in both intellectually and academically rigorous courses and a residential environment that acknowledge and value diversity, inclusion, difference, equity, and justice. Students study the broader contexts of economic inequality, racism, sexism, and human rights (for example) to better understand the complex interplay between their social identities and senses of agency as they navigate their worlds. Emphasis is placed on honing student reading and writing skills (quantitative reasoning is incorporated to the degree possible within the courses offered in a given summer).

Latest Applied News

Navigation is an important cognitive task that enables humans and animals to traverse, without maps, over long distances in a complex world. Such long-range navigation can simultaneously support self-localisation (“I am here”) and a representation of the goal (“I am going there”).

In
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,we present an interactive navigation environment that uses first-person perspective photographs from
Google Street View
and gamify that environment to train an AI. As standard with Street View images, faces and license plates have been blurred and are unrecognisable. We build a neural network-based artificial agent that learns to navigate multiple cities using visual information (pixels from a Street View image). Note that this research is about navigation in general rather than driving; we did not use traffic information nor try to model vehicle control.

The agent is rewarded when it reaches a target destination (specified, for instance, as pair of latitude and longitude coordinates), like a courier tasked with an endless set of deliveries but without a map. Over time, the AI agent learns to cross entire cities in this way. We also demonstrate that our agent can learn the task in multiple cities, and then robustly adapt to a new city.

Learning navigation without building maps

We depart from the traditional approaches which rely on explicit mapping and exploration (like a cartographer who tries to localise themselves and draw a map at the same time). Our approach, in contrast, is to learn to navigate as humans used to do, without maps, GPS localisation, or other aids, using only visual observations. We build a neural network agent that inputs images observed from the environment and predicts the next action it should take in that environment. We train it end-to-end using deep reinforcement learning, similarly to some recent work on
learning to navigate in complex 3D mazes
and
reinforcement learning with unsupervised auxiliary tasks
for playing games. Unlike those studies, which were conducted on small-scale simulated maze environments, we utilise city-scale real-world data, including complex intersections, footpaths, tunnels, and diverse topology across London, Paris, and New York City. Moreover, the approach we use support city-specific learning and optimisation as well as general, transferable navigation behaviours.

Modular neural network architecture that can transfer to new cities

The neural network inside our agent consists of three parts: 1) a convolutional network that can process images and extract visual features, 2) a locale-specific recurrent neural network that is implicitly tasked with memorising the environment as well as learning a representation of “here” (current position of the agent) and of “there” (location of the goal) and 3) a locale-invariant recurrent network that produces the navigation policy over the agent’s actions. The locale-specific module is designed to be interchangeable and, as its name indicates, unique to each city where the agent navigates, whereas the vision module and the policy module can be locale-invariant.

Just as in the Google Street View interface, the agent can rotate in place or move forward to the next panorama, when possible. Unlike the Google Maps and Street View environment, the agent does not see the little arrows, the local or global map, or the famous Pegman: it needs to learn to differentiate open roads from sidewalks. The target destinations may be kilometres away in the real world and require the agent to step through hundreds of panoramas to reach them.

We demonstrate that our proposed method can provide a mechanism for transferring knowledge to new cities. As with humans, when our agent visits a new city, we would expect it to have to learn a new set of landmarks, but not to have to re-learn its visual representations or its behaviours (e.g., zooming forward along streets or turning at intersections). Therefore, using the MultiCity architecture, we train first on a number of cities, then we freeze both the policy network and the visual convolutional network and only a new locale-specific pathway on a new city. This approach enables the agent to acquire new knowledge without forgetting what it has already learned, similarly to the
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architecture.

Studying navigation is fundamental in the study and development of artificial intelligence, and trying to replicate navigation in artificial agents can also help scientists understand its biological underpinnings.

How to Punctuate Bulleted and Numbered Lists

Last updated: 29 June, 2018

Bulleted and numbered lists help you present your work clearly. You can use them to

The advantage of lists is that they jump out at readers. However, if lists contain typos or inconsistencies then those will jump out at readers too. So it’s crucial to format and punctuate lists correctly, or your entire document can look sloppy.

Figure 1: Format and punctuate lists correctly

In the rest of this article, the term ‘bulleted lists’ is used to refer to both bulleted and numbered lists because in most cases the rules governing these two styles of list are identical.

In multi-author documents, inconsistencies in bullet punctuation are a constant concern. Authors often have their own preferred style of punctuation. For example, one author might prefer list items to end in semicolons, while another prefers full stops.

It’s challenging enough for each author to apply their own rules consistently. However, punctuation marks are small and can be difficult to spot. As a result, ensuring consistency between authors is even more challenging, so mistakes happen frequently.

So, how should you punctuate bulleted lists? As with so many writing issues, the answer is ‘It depends’! There is no single right way to punctuate all lists. It can even be right to use semicolons and full stops in the same document, because different types of list can take different forms of punctuation.

If you’re trying to figure out how to punctuate a list, the best place to start is your style manual. Preferences vary from guide to guide. For example, the European Union’s
English Style Guide
suggests

On the other hand, the Australian
Style Manual
(Wiley, Sixth Edition) suggests no punctuation unless there is a full sentence (or multiple sentences), in which case there should be a full stop.

When you check your style guide, pay particular attention to the rules for using

To give you a better idea, here are some examples.

However, you might also see some lists (where each item contains a verb but there are no items with multiple sentences) presented as follows:

Alongside bulleted lists like these, you might also see this type (where each item is just one word and there is no verb):

Even though the three lists above all use different punctuation, they are consistent because the punctuation style in each case fits the type of list.

Some errors are worse than others. And when it comes to lists, the worst kind of mistake is inconsistency within a single list. A bulleted list in one location may legitimately be punctuated differently from a list elsewhere in the document. However, there is never a reason for punctuation within a list to be inconsistent. For example, the first item in a list should never end in a full stop if the second item ends with a semicolon. One of those must be wrong! So, while you’re checking, concentrate first on consistency within lists.